The present disclosure relates to computer learning, and more specifically, to the application of computer learning to digital data compression.
An Artificial Neural Network (ANN) may be an information processing paradigm, inspired by the way the biological nervous system, from an individual neuron to a brain, processes information. An important element of the ANN paradigm is the structure of the information processing system. The system may be composed of a large number of highly interconnected processing elements (representing individual neurons in the biological model). These interconnected processing elements may work in unison to solve specific problems. ANNs may learn by example.
As the technological capacity for the creation, tracking, and retention of data continues to grow, data compression has developed as a technology for improving transmission and storage of the vast amounts of a data being created and shared. Data compression may allow for the reduction of data size by representing data differently. The data may be restored at a later time (for example, following transmission or recall from memory) for further processing. At times, the ability to select a compression technique and effectively perform a data compression may be limited by resource constraints, as data compression can be quite burdensome on a system's resources.